Mohammad Abazari's repositories
composite_cdm_ap_ply
Continuum damage mechanics framework for AP-PLY composites implemented as an Abaqus VUMAT subroutine.
Algorithm
Differential evolution; Particle swarm optimization; Simulated annealing; Jaya
Answer-to-History-Mohammad-Reza-Pahlavi-1979
"Answer to History" is the last book M.R. Pahlavi published just before his passing by STEIN AND DAY Publishers New York.
composite_cdm_tan
3D continuum damage mechanics model for composite materials implemented in Fortran (Abaqus Explicit VUMAT).
CPFEM-VUMAT
Crystal plasticity finite element code, VUMAT file for Abaqus
Design-and-development-of-hybrid-Optimization-enabled-Deep-Q-learning-model-for-Covid-19-detection-
The problem of respiratory sound classification has received good attention from the clinical scientists and medical researcher’s community in the last year to the diagnosis of COVID-19 disease. In this paper, the input audio samples are fed into the pre-processing module in which median filtering is done to remove the noise and artifacts from the audio samples. The feature extraction is carried out by considering features, like spectral contrast, Mel frequency cepstral coefficients (MFCC), Empirical Mode Decomposition (EMD) algorithm, spectral flux, Fast Fourier Transform (FFT), spectral roll-off, spectral centroid, Root mean square energy, zero-crossing rate, spectral bandwidth, spectral flatness, power spectral density, mobility complexity, fluctuation index and relative amplitude. Moreover, the deep Q network is applied for Covid-19 classification phase wherein the training of deep Q network is done using the proposed optimization algorithm, named Snake Jaya Honey Badger Optimization (SJHBO) algorithm. The proposed SJHBO algorithm is the hybridization of Jaya Honey Badger Optimization (JHBO) along with Snake optimization. Hence, the developed method achieved the better superior performance based on the accuracy, sensitivity and specificity .
Fatigue-life-prediction-of-aluminum-alloy
Fatigue-life-prediction-of-aluminum-alloy
fatiguepy
Package to estimate life of random fatigue history with frequency domain methods
Flywheel_Shape_Optim
MATLAB code for shape optimization of flywheel using JAYA algorithm.
Jaya-Honey-Badger-Optimization-based-Deep-Neuro-Fuzzy-Network-structure-for-detection-of-Covid-19-
The Covid-19 virus is fast spreading disease in globally, which threateness billions of human begins. In this paper, Jaya Honey Badger Optimization-based Deep Neuro Fuzzy Network (JHBO-based DNFN) is introduced for Covid-19 prediction by audio signal. Here, Covid-19 prediction is done using DNFN, and it is trained by developed JHBO algorithm. The developed JHBO-based DNFN is outperformed than other existing methods testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219. The Covid-19 prediction process is more indispensable to handle the spread and death occurred rate because of Covid-19. However, early and precise prediction of Covid-19 is more difficult, because of different sizes and resolutions of input image. An effective Covid-19 detection technique is introduced based on hybrid optimization driven deep learning model. The Deep Neuro Fuzzy network (DNFN) is used for detecting Covid-19, which classifies the feature vector as Covid-19 or non Covid-19. Moreover, the DNFN is trained by devised Jaya Honey Badger Optimization (JHBO) approach, which is introduced by combining Honey Badger optimization Algorithm (HBA) and Jaya algorithm. The developed JHBO-based DNFN is outperformed than other existing methods testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219. Covid-19 is respiratory disease, which is usually produced by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). However, it is more indispensable to detect the positive cases for reducing further spread of virus, and former treatment of affected patients. An effectual Covid-19 detection model using devised Jaya Honey Badger Optimization-based Deep Neuro Fuzzy Network (JHBO-based DNFN) is developed in this paper. Here, the audio signal is considered as input for detecting Covid-19. The gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed JHBO algorithm. Accordingly, the developed JHBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm. The performance of developed Covid-19 detection model is evaluated using three metrics, like testing accuracy, sensitivity and specificity. The developed JHBO-based DNFN is outperformed than other existing methods testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219. The recent investigation has started for evaluating the human respiratory sounds, like voice recorded, cough, and breathing from hospital confirmed Covid-19 tools, which differs from healthy persons sound. The cough-based detection of Covid-19 also considered with non-respiratory and respiratory sounds data related with all declared situations. This paper explicates the Covid-19 detection approach using designed Jaya Honey Badger Optimization-based Deep Neuro Fuzzy Network (JHBO-based DNFN) with audio sample. The series of steps followed for introduced Covid-19 diagnosis model are pre-processing, feature extraction, and classification. The input audio sample is acquired from a Coswara dataset and gaussian filter is applied. The gaussian filter effectively reduces the salt and pepper noise with minimal duration. Feature extraction process is most significant for precise detection of Covid-19, where spectral bandwidth, spectral roll off, Spectral flatness, Mel frequency cepstral coefficients (MFCC), spectral centroid, root mean square energy, spectral contract, and zero crossing rate are extracted. The Deep learning approach is effectual for disease detection and classification process in medical field. Here, DNFN is utilized for detecting the Covid-19 disease. Moreover, DNFN is trained by developed JHBO approach for obtaining better performance. The proposed JHBO algorithm is newly devised by combining Jaya algorithm and HBA. Here, Jaya algorithm is incorporated with HBA for obtaining improved performance with better convergence speed. The performance of DNFN is estimated with three performance metrics, namely specificity, testing accuracy and sensitivity. The proposed JHBO-based DNFN achieved improved performance testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219.
large-scale-truss-optimization
Large scale truss optimization using NSGA-II
MATH307
Applied Linear Algebra
mathematical-python
Introduction to Mathematical Computing with Python and Jupyter
MATLAB-SDOF-Solver
Solve SDOF with any loading function
Mohammad-Abazari
Config files for my GitHub profile.
pinn_wind_bearing
Python scripts for wind turbine main bearing fatigue life estimation with physics-informed neural networks
pyMetaheuristic
A python library for: Adaptive Random Search, Ant Lion Optimizer, Arithmetic Optimization Algorithm, Artificial Bee Colony Optimization, Artificial Fish Swarm Algorithm, Bat Algorithm, Biogeography Based Optimization, Cross-Entropy Method, Crow Search Algorithm, Cuckoo Search, Differential Evolution, Dispersive Flies Optimization, Dragonfly Algorithm, Firefly Algorithm, Flow Direction Algorithm, Flower Pollination Algorithm, Genetic Algorithm, Grasshopper Optimization Algorithm, Gravitational Search Algorithm, Grey Wolf Optimizer, Harris Hawks Optimization, Improved Grey Wolf Optimizer, Improved Whale Optimization Algorithm, Jaya, Jellyfish Search Optimizer, Krill Herd Algorithm, Memetic Algorithm, Moth Flame Optimization, Multiverse Optimizer,Pathfinder Algorithm, Particle Swarm Optimization, Random Search, Salp Swarm Algorithm, Simulated Annealing, Sine Cosine Algorithm, Teaching Learning Based Optimization, Whale Optimization Algorithm.
Python_Stable_3D_Truss_Analysis
slientruss3d : Python for stable truss analysis and optimization tool
SC-yield-function
A C2 continuous yield funciton for basic shear strength, tensile strength cut-off, compressive strength cap, and the impact of intermediate principal stress
The-source-code-of-enhanced-Jaya-algorithm-for-global-optimization
The-source-code-of-enhanced-Jaya-algorithm-for-global-optimization
truss-bridge-optimization
truss-bridge-optimization
Truss-Optimization-1
Truss-Optimization-1+
Truss-Optimization-using-genetic-Algorithm
Light Weighting of A Truss System using Genetic Algorithm.
TrussDesigner
Models and optimizes trusses
TrussLayoutOptimization
TrussLayoutOptimization
trussOptimization-1
trussOptimization-1
TTO
Truss topology optimization
USDFLD-Tsai-Wu
USDFLD Tsai-Wu
ViscoelasticFoam
Abaqus VUMAT and sample input files for modeling of viscoelastic foams
VUMAT-Implementation-in-ABAQUS-Explicit
Developed a finite deformation VUMAT in Fortran to implement von-Mises plasticity for an elastic-isotropic hardening material and then validated the results of the code with ABAQUS inbuilt model using Standard test cases